Experimental Validation of Iterative Learning Control for DC/DC Power Converters

Bingqiang Li, Saleem Riaz () and Yiyun Zhao
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Bingqiang Li: School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Saleem Riaz: School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
Yiyun Zhao: School of Automation, Northwestern Polytechnical University, Xi’an 710072, China

Energies, 2023, vol. 16, issue 18, 1-16

Abstract: In order to solve the problem that the parameters of traditional proportional–integral (PI) control are not easy to adjust, an iterative learning control (ILC) technique for a DC/DC power converter is proposed in this paper. Firstly, we have developed a system which is composed of two different states of DC/DC converter in order to obtain its equivalent linear time-varying system, and then the open-loop PD-type ILC law has been used to control it. Secondly, an experimental setup is arranged to verify and compare the simulated results. The experimental results show that, as compared with the traditional PI control, the proposed strategy is easy to implement and optimal with regard to debugging parameters, and it can achieve zero steady-state tracking errors without overshooting. Finally, the experimental results have also proven that our proposed scheme of iterative learning control for a DC/DC power converter is robust as compared to traditional PI control.

Keywords: DC/DC converters; iterative learning control (ILC); traditional PI control; robust control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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